Just a quick note on the news from the development team:

- Our first implementation of TGVRestoration was modeled with a Gaussian noise distribution as basis. So, it worked very well with high SNR images, and "normal" images. Astronomical deep sky images, on the other hand, suffered from some serious ringing. We included some deringing functions to mitigate this problem, and achieve more robust results, but were not as good as I expected (and I had very high expectations for this... I think that it is better than regularized RL, but more work is needed).

- The tests on deep sky images derived in a reformulation of the TGV regularization algorithm, both for the Denoise and Restoration problems. A new method was designed for a Poisson noise model. Right now we are close to publish a new TGVDenoise, with several changes. Now the tool is much more flexible, and may adapt its behavior for many noise distributions. It has in-built 3 statistical noise models, Gaussian, Poisson and a L1 norm, with a new flexible edge protection. We are polishing the interface elements and working on some examples to accompany the new release.

- The development of the TGVRestoration tool has being delayed a couple of weeks, until we have the new TGVDenoise ready for release. The new TGVR will also incorporate several statistical models. Right now we are testing a mixed L1/L2 Norm (for Gaussian noise), and three solvers for the Poisson model. One of them is a regularized Richardson-Lucy iteration, with a modified gradient, following the classic TV algorithm. Another is a two step Expectation-Maximization (similar to regularized RL), with a TGV step. The last is a new derivation of the Chambolle/Pock primal dual algorithm for Poisson noise. So, a lot is going on here.

Please, have a little more patience. We are working to create a very powerful and flexible tool. We still need to evaluate the efficiency of our new deringing methods, so more time is needed. I may publish an unofficial release in my development server, asking for beta testing. Stay tuned.